Artificial Intelligence and Intellectual Property Rights: Comparative Transnational Policy Analysis

Artificial intelligence's rapid integration with intellectual property rights necessitates assessment of its impact on trade secrets, copyrights and patents. This study addresses lacunae in existing laws where India lacks AI-specific provisions, creating doctrinal inconsistencies and enforcement inefficacies. Global discourse on AI-IPR protections remains nascent. The research identifies gaps in Indian IP laws' adaptability to AI-generated outputs: trade secret protection is inadequate against AI threats; standardized inventorship criteria are absent. Employing doctrinal and comparative methodology, it scrutinizes legislative texts, judicial precedents and policy instruments across India, US, UK and EU. Preliminary findings reveal shortcomings: India's contract law creates fragmented trade secret regime; Section 3(k) of Indian Patents Act blocks AI invention patenting; copyright varies in authorship attribution. The study proposes harmonized legal taxonomy accommodating AI's role while preserving innovation incentives. India's National AI Strategy (2024) shows progress but legislative clarity is imperative. This contributes to global discourse with AI-specific IP protections ensuring resilience and equitable innovation. Promising results underscore recalibrating India's IP jurisprudence for global alignment.

Criminal Liability in AI-Enabled Autonomous Vehicles: A Comparative Study

AI revolutionizes transportation through autonomous vehicles (AVs) but introduces complex criminal liability issues regarding infractions. This study employs a comparative legal analysis of primary statutes, real-world liability claims, and academic literature across the US, Germany, UK, China, and India; jurisdictions selected for their technological advancement and contrasting regulatory approaches. The research examines the attribution of human error, AI moral agency, and the identification of primary offenders in AV incidents. Findings reveal fragmented regulatory landscapes: India and the US rely on loose networks of state laws, whereas the UK enacted the pioneering Automated and Electric Vehicles Act 2018. Germany enforces strict safety standards, distinguishing liability based on the vehicle's operating mode, while China similarly aims for a stringent liability regime. The study concludes that globally harmonized legal standards are essential to foster technological innovation while ensuring minimum risk and clear liability attribution.

Algorithmic Criminal Liability in Greenwashing: Comparing India, United States, and European Union

AI-powered greenwashing has emerged as an insidious challenge within corporate sustainability governance, exacerbating the opacity of environmental disclosures and subverting regulatory oversight. This study conducts a comparative legal analysis of criminal liability for AI-mediated greenwashing across India, the US, and the EU, exposing doctrinal lacunae in attributing culpability when deceptive claims originate from algorithmic systems. Existing statutes exhibit anthropocentric biases by predicating liability on demonstrable human intent, rendering them ill-equipped to address algorithmic deception. The research identifies a critical gap in jurisprudential adaptation, as prevailing fraud statutes remain antiquated vis-à-vis AI-generated misrepresentation. Utilising a doctrinal legal methodology, this study systematically dissects judicial precedents and statutory instruments, yielding results regarding the potential expansion of corporate criminal liability. Findings underscore the viability of strict liability models, recalibrated governance frameworks for AI accountability, and algorithmic due diligence mandates under ESG regimes. Comparative insights reveal jurisdictional disparities, with the EU Corporate Sustainability Due Diligence Directive (CSDDD) offering a potential transnational model. This study contributes to AI ethics and environmental jurisprudence by advocating for a hybrid liability framework integrating algorithmic risk assessment with legal personhood constructs, ensuring algorithmic opacity does not preclude liability enforcement.